Secure Multi-Party Computation of Graphs’ Intersection and Union under the Malicious Model

Author:

Liu Xin12ORCID,Tu Xiao-Fen2,Luo Dan1,Xu Gang3,Xiong Neal4ORCID,Chen Xiu-Bo5

Affiliation:

1. Department of Computer Science and Technology, Tianjin Ren’ai College, Tianjin 733299, China

2. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

3. School of Information Science and Technology, North China University of Technology, Beijing 100144, China

4. Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX 79830, USA

5. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

In recent years, with the development of information security, secure multi-party computation has gradually become a research hotspot in the field of privacy protection. The intersection and union computation of graphs is an important branch of secure computing geometry. At present, the intersection and union of graphs are almost designed under the semi-honest model, and few solutions are proposed under the malicious model. However, the solution under the malicious model is more secure and has important theoretical and practical significance. In this paper, the possible malicious behaviors of computing the intersection and union of graphs are analyzed. Using the Lifted-ElGamal threshold cryptosystem and zero-knowledge proof method, the secure multi-party computation algorithm of graphs’ intersection and union under the malicious model is designed. The real/ideal model paradigm is used to prove the security of the algorithm, the efficiency of the algorithm is analyzed in detail, and the feasibility is verified through experiment.

Funder

National Natural Science Foundation of China: Big Data Analysis based on Software Defined Networking Architecture

NSFC

Inner Mongolia Natural Science Foundation

2023 Inner Mongolia Youth Science and Technology Talents Development Project

2022 Fund Project of Central Government Guiding Local Science and Technology Development

2022 Basic Scientific Research Project of Direct Universities of Inner Mongolia

2022 “Western Light” Talent Training Program “Western Young Scholars” Project

2022 Inner Mongolia Postgraduate Education and Teaching Reform Project

2022 Ministry of Education Central and Western China Young Backbone Teachers and Domestic Visiting Scholars Program

Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Open Project Fund

Baotou Kundulun District Science and Technology Plan Project

Inner Mongolia Science and Technology Major Project

Fundamental Research Funds for Beijing Municipal Commission of Education

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privacy Attacks and Defenses in Machine Learning: A Survey;Lecture Notes in Electrical Engineering;2024

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